Real-Time Predictive Transit

Haris N. Kout­sopoulos believes that our ability to pre­dict the future has the poten­tial to enable var­ious inno­va­tions in public transit. The short-​​term future, that is.

Is a subway sta­tion or train about to get over­crowded during rush hour? Is an impending storm about to wreak havoc on the system, or is a bus sta­tion about to be flooded with fans leaving a base­ball game or con­cert? Should I, as a com­muter, con­sider taking an alter­na­tive route home or leaving work a bit later?

These are the types of public transit ques­tions Kout­sopoulos, pro­fessor ofcivil and envi­ron­mental engi­neering at North­eastern, is focused on addressing by way of real-​​time pre­dic­tive analysis. In one project involving Trans­port for London—the body that over­sees London’s transit system, including the London Under­ground, which is one of the world’s busiest metro systems—he and Peyman Nour­salehi, one of his doc­toral stu­dents, are devel­oping real-​​time pre­dic­tive models that fore­cast subway transit activity 15 or 30 min­utes into the future. Such models, he explains, are based upon analyses of large swaths of auto­mated fare-​​collection data that can reveal past travel pat­terns as well as real-​​time fac­tors such as weather, events, and even­tu­ally, even social media chatter. Kout­sopoulos’ team has devel­oped an ini­tial pro­to­type of a pre­dic­tive model as well as an accom­pa­nying visu­al­iza­tion tool.

“A lot of the work with this data has basi­cally looked at what hap­pened yes­terday, where pas­sen­gers enter and where they exit the system,” Kout­sopoulos says. “Now what we’re thinking about is what can we learn from the travel pat­terns in all this data and using what we learn from the past to make short-​​term pre­dic­tions about the future. It’s about being proac­tive, not reac­tive. For example, some­times in those sys­tems, if a sta­tion gets too crowded, the gates are closed and pas­sen­gers aren’t allowed to come in until the crowd sub­sides. This is reac­tive. But if you can pre­dict that demand will increase in the near future, maybe you can take action ear­lier and pre­vent the problem from becoming bigger later on.”

Now what we’re thinking about is what can we learn from the travel pat­terns in all this data and using what we learn from the past to make short-​​term pre­dic­tions about the future. It’s about being proac­tive, not reac­tive.— Pro­fessor Haris Koutsopoulos

The goal of Kout­sopoulos’ research is to develop tools that help transit oper­a­tors opti­mally manage their sys­tems and com­muters make informed trip deci­sions. For example, he says, pre­dic­tive ana­lytics could be used for real-​​time gate man­age­ment to pre­vent over­crowding at major sta­tions or feed into a transit app that sends real-​​time pre­dic­tive infor­ma­tion to riders.

The London project is part of Kout­sopoulos’ work in the MIT-​​NEU Transit Lab, a col­lab­o­ra­tion between North­eastern and the Mass­a­chu­setts Insti­tute of Tech­nology. In addi­tion to Trans­port for London, Kout­sopoulos and his doc­toral stu­dents are studying how pas­sen­gers on the MTR in Hong Kong use the under­ground transit system with the goal of devel­oping strate­gies to alle­viate con­ges­tion in the main parts of the net­work. These strate­gies include helping oper­a­tors improve crowd man­age­ment and incen­tivizing riders to alter their travel patterns.

Kout­sopoulos and his stu­dents are also pur­suing sep­a­rate, but related, research focused on observing how dif­ferent riders use a transit system and then infer­ring under­lying traits about these travel behav­iors. By clus­tering riders into dif­ferent groups based on these behav­iors, he explains, you can better under­stand rid­er­ship patterns—and there­fore improve your pre­dic­tive models.

Haris Kout­sopoulos, pro­fessor of civil and envi­ron­mental engi­neering, stands at North­eastern sta­tion on the MBTA Green Line. Photo by Matthew Modoono/​Northeastern University

A traffic sim­u­la­tion pioneer

His work rep­re­sents an example of the next-​​generation of how we think about transportation—using Big Data to make informed deci­sions about how, when, and where people move. Kout­sopoulos describes his research as being focused pri­marily on intel­li­gent trans­porta­tion sys­tems. As he puts it, “The idea is to use tech­nology to improve how well we use the capacity that is actu­ally avail­able in the system to min­i­mize inefficiencies.”

Kout­sopoulos has been a pio­neer in the field of traffic sim­u­la­tion mod­eling for more than 20 years, and ear­lier this year he was hon­ored with the Traffic Sim­u­la­tion Life­time Achieve­ment Award by the Trans­porta­tion Research Board.

Prior to joining North­eastern in 2014, Kout­sopoulos founded the iMo­bility lab at the KTH Royal Insti­tute of Tech­nology in Stock­holm, where he used real-​​time GPS data from taxis to develop traffic man­age­ment and pre­dic­tion tools for local authorities.

As a glob­ally renowned researcher in this field, Kout­sopoulos recently co-​​hosted an inter­na­tional con­fer­ence at North­eastern, called TransitData2016, where scholars and trans­porta­tion offi­cials world­wide con­vened to dis­cuss new research and advance­ments cen­tered on using data from auto­mated sources to improve plan­ning and oper­a­tions at public transit sys­tems. He noted that three trends he observed were more dis­cus­sion about data fusion—using data from a variety of sources to make trans­porta­tion man­age­ment, evi­dence based decisions—increased use of visu­al­iza­tion tools by researchers and prac­ti­tioners to better com­mu­ni­cate how trans­porta­tion sys­tems are func­tioning, and increased interest by agen­cies in data ware­houses and open data.

“We view all the work we’re doing with transit agen­cies as building blocks that they can use to improve ser­vice, be more respon­sive, com­mu­ni­cate better, plan their sys­tems better, and overall be more com­pet­i­tive in pro­viding mobility options,” Kout­sopoulos says.

CAMD/CEE Assistant Professor David Fannon is working with Michelle Laboy and Peter Wiederspahn from the School of Architecture to design buildings that can stand the test of time and can be easily converted to meet future needs.

CEE Chair and Professor Jerome Hajjar and ECE Associate Professor Taskin Padir are collaborating on a research project for post-disaster assessment of critical infrastructure such as bridges using small unmanned aircraft systems.